Differential diagnosis of hepatocellular carcinoma from metastatic tumors in the liver using microRNA expression

Barshack, Iris; Meiri, Eti; Rosenwald, Shai; Lebanony, Danit; Bronfeld, M.; Aviel‐Ronen, Sarit; Rosenblatt, Kinneret; Polak‐Charcon, Sylvie; Leizerman, Ilit; Ezagouri, Meital; Zepeniuk, Merav; Shabes, Norberto; Cohen, Lahav; Tabak, Sarit; Cohen, Dalia; Bentwich, Zvi; Rosenfeld, Nitzan

doi:10.1016/j.biocel.2009.02.021

Cited by 49 publications

(36 citation statements)

References 56 publications

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“…MicroRNAs were selected based on their expression levels and distributions in the training set (Table 1), with the aim of selecting microRNAs that provide a distinct difference in expression that can be used for accurate classification. 30,37,41 For identifying between the pair of types (chromophobe, oncocytoma) and the pair (conventional, papillary), we chose hsa-miR-221 and hsa-miR-210; for identifying between chromophobe and oncocytoma, we chose hsa-miR-200c and hsa-miR-139-5p; and for identifying between papillary and conventional, we chose hsa-miR-31 and hsa-miR-126 (Figure 3). Using one microRNA from each set is sufficient to obtain a clear separation between the four groups ( Figure 3A), but to provide internal normalization and ensure better performance we used a combination of two microRNAs with complementary specificities at each decision point.…”

Section: ) and Papillary Tumors (N ϭ 20)mentioning

confidence: 99%

Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression

Fridman

Dotan

Barshack

et al. 2010

The Journal of Molecular Diagnostics

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103

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Subtypes of renal tumors have different genetic backgrounds, prognoses, and responses to surgical and medical treatment, and their differential diagnosis is a frequent challenge for pathologists. New biomarkers can help improve the diagnosis and hence the management of renal cancer patients. We extracted RNA from 71 formalin-fixed paraffin-embedded (FFPE) renal tumor samples and measured expression of more than 900 microRNAs using custom microarrays. Clustering revealed similarity in microRNA expression between oncocytoma and chromophobe subtypes as well as between conventional (clear-cell) and papillary tumors. By basing a classification algorithm on this structure, we followed inherent biological correlations and could achieve accurate classification using few microRNAs markers. We defined a two-step decision-tree classifier that uses expression levels of six microRNAs: the first step uses expression levels of hsa-miR-210 and hsa-miR-221 to distinguish between the two pairs of subtypes; the second step uses either hsa-miR-200c with hsa-miR-139-5p to identify oncocytoma from chromophobe, or hsa-miR-31 with hsa-miR-126 to identify conventional from papillary tumors. The classifier was tested on an independent set of FFPE tumor samples from 54 additional patients, and identified correctly 93% of the cases. Validation on qRT-PCR platform demonstrated high correlation with microarray results and accurate classification. MicroRNA expression profiling is a very effective molecular bioassay for classification of renal tumors and can offer a quantitative standardized complement to current methods of tumor classification.

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Section: ) and Papillary Tumors (N ϭ 20)mentioning

confidence: 99%

Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression

Fridman

Dotan

Barshack

et al. 2010

The Journal of Molecular Diagnostics

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103

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“…Although studies have reported promising treatment strategies for HCC, the poor outcome remains unchanged, with a median survival of most patients of 6-9 months following diagnosis [3]. Currently, molecular profiling of HCC has suggested that a set of genes are deregulated in the development of HCC, and that the expression levels of these genes are correlated with HCC diagnostic classification, prognosis, and biological targeted therapy [4][5][6][7]. Research to identify the deregulated genes in HCC and to elucidate their roles in HCC carcinogenesis and progression is ongoing.…”

Section: Introductionmentioning

confidence: 99%

MicroRNA‐486‐5p, which is downregulated in hepatocellular carcinoma, suppresses tumor growth by targeting PIK3R1

et al. 2014

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Deregulated microRNAs and their roles in carcinogenesis and cancer progression have attracted much attention. In previous studies conducted in our laboratory, the Illumina Solexa massively parallel signature sequencing of miRNomes in nontumor and hepatocellular carcinoma (HCC) tissues revealed that miR-486-5p was significantly downregulated in HCC, but its role in HCC development remains unknown. In this study, miR-486-5p levels in HCC tissues and matched control tissues, and in seven HCC cell lines (QGY-7701, QGY-7703, QGY-7404, SMMC-7721, Huh7, HepG2, and PCL/PRF/5) and human normal liver cells (HL-7702), were tested by real-time quantitative RT-PCR. We found that the level of miR-486-5p was significantly decreased in HCC tissue and in all seven HCC cell lines. Overexpression of miR-486-5p markedly suppressed HCC cell proliferation, migration and invasion in vitro, and inhibited HCC growth in vivo. Mechanistically, miR-486-5p was confirmed to directly target PIK3R1 expression, thereby suppressing phosphatidylinositol 3-kinase-AKT pathway activation, by dual luciferase reporter assay and real-time quantitative RT-PCR and western blot analysis. In addition, PIK3R1 knockdown mimicked the effects of miR-486-5p overexpression by inhibiting HCC growth, migration, and invasion. Furthermore, correlation analysis, Kaplan-Meier estimates and Cox proportional hazard models showed an inverse correlation between miR-486-5p and PIK3R1, as well as a shorter time to recurrence after HCC resection, in patients with lower miR-486-5p expression. Hence, we conclude that miR-486-5p, which is frequently downregulated in HCC, inhibits HCC progression by targeting PIK3R1 and phosphatidylinositol 3-kinase-AKT activation. DatabaseThe Solexa sequencing data are available in GEO database (www.ncbi.nlm.nih.gov/geo/) under accession number GSE21279.Abbreviations DFS, disease-free survival; FACS, fluorescence-activated cell sorting; GSK3b, glycogen synthase kinase-3b; HCC, hepatocellular carcinoma; miRNA, microRNA; MPSS, massively parallel signature sequencing; PI3K, phosphatidylinositol 3-kinase; qRT-PCR, quantitative RT-PCR; SD, standard deviation.

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“…One microarray analysis applying to microRNAs was performed to distinguish hepatocellular carcinoma from metastatic tumors in the liver [21]. The study demonstrated that two microRNAs, hsa-miR-141 and hsa-miR-200c had significant higher level in non-hepatic epithelial tumors to promote epithelial phenotypes while endothelial-associated hsa-miR-126 showed higher expression levels in hepatocellular carcinomas in contrast.…”

Section: High-throughput Analysis For Profi-ling Of Cancer Transcriptmentioning

confidence: 98%

Integrative System Biology Strategies for Disease Biomarker Discovery

Zhang¹,

Hu²,

Deng³

et al. 2012

CCHTS

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Biomarkers are currently widely used to diagnose diseases, monitor treatments, and evaluate potential drug candidates. Research of differential Omics accelerate the advancements of biomarkers' discovery. By extracting biological knowledge from the 'omics' through integration, integrative system biology creates predictive models of cells, organs, biochemical processes and complete organisms, in addition to identifying human disease biomarkers. Recent development in high-throughput methods enables analysis of genome, transcriptome, proteome, and metabolome at an unprecedented scale, thus contributing to the deluge of experimental data in numerous public databases. Several integrative system biology approaches have been developed and applied to the discovery of disease biomarkers from databases. In this review, we highlight several of these approaches and identify future steps in the context of the field of integrative system biology.

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Differential diagnosis of hepatocellular carcinoma from metastatic tumors in the liver using microRNA expression

Cited by 49 publications

References 56 publications

Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression

Accurate Molecular Classification of Renal Tumors Using MicroRNA Expression

MicroRNA‐486‐5p, which is downregulated in hepatocellular carcinoma, suppresses tumor growth by targeting PIK3R1

Integrative System Biology Strategies for Disease Biomarker Discovery

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